In many of today’s top enterprises, the data lake is becoming a huge topic of conversation. Across industries like finance, manufacturing, and healthcare, the Internet of Things (IoT) allows data to be collected and aggregated from more sources than ever before. For these enterprises, the primary goals of collecting data are to accelerate innovation, improve operational efficiency, improve sustainability, reduce risks, and ultimately improve quality of life. To achieve these goals, enterprises are looking for ways to help their data scientists get the most value out of their data at a faster pace and stay ahead in their industry.
And the velocity and requirements for data analytics, machine learning, and artificial intelligence have been increasing. According to Forbes, 90% of the world’s data was generated in the last 2 years. It’s clear that enterprise data needs will continue to grow rapidly. NetApp is highly motivated to help our customers build resilient, feature-rich data pipelines—with the flexibility to adapt to evolving requirements and scale easily in the future.
Maintaining a data lake involves many complex manual tasks. But in a modern data lake, these tasks can be simplified and automated to make workflows more efficient and effective. These tasks include collecting, ingesting, sanitizing, moving, and cataloging datasets—and securely making these datasets available to analytics and machine learning applications. Today, many of our customers are looking into Simple Storage Service (S3) object storage for their data lakes, because object storage holds unmatched advantages over other options like NAS and HDFS. Object storage platforms have evolved over the past few years to deliver the performance, durability, and scale needed for analytics and machine learning applications. A modern data lake that uses object storage will break down silos, enabling data scientists to maximize value by consolidating different types of structured, semi-structured, and unstructured data in one accessible source.
The industry-leading, enterprise-grade NetApp® StorageGRID® object-based storage solution is well positioned to support today’s analytics and machine learning workloads. It’s built-in information lifecycle management engine differentiates StorageGRID from other on-premises object storage platforms. And because StorageGRID solutions can leverage compute services, whether it’s in a private or public cloud, data scientists have the flexibility to build cost-efficient and resource-efficient data pipelines. In addition, by separating compute and storage, StorageGRID helps lower the overall TCO of analytics and machine learning applications, because now IT teams can scale compute and storage independently.
When you build your data lake on StorageGRID, you get the following benefits:
Enterprises that want to help their data scientists build a cost-effective data pipeline will see the benefits of incorporating StorageGRID into their data lakes. StorageGRID has been on the market for over 20 years now, starting with a DICOM medical imagery storage and management solution for healthcare companies. Ever since, StorageGRID has been expanding support for new use cases. As the industry changes, StorageGRID continues to adapt and innovate to provide our customers with industry-leading advantages and to support changing requirements.
To learn more about how NetApp can help your team modernize your data architecture, check out our infographic on how to get where you need to be in this competitive market.
Joseph Kandatilparambil is Technical Marketing Engineer for StorageGRID, with over 7 years of experience in the storage industry. Joseph helps with customer driven innovation by empowering customers with solutions that help them focus on driving their product forward and expand their horizons. Outside of work, Joseph enjoys kite-surfing, rock climbing and hiking.
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